{
  "abstract": "Cellular state transitions underlie adaptation, ageing and disease, yet prioritizing catalogued genetic perturbations whose expression signatures match an observed transcriptomic shift remains difficult. Most models predict phenotype from a nominated intervention, whereas genetic inverse benchmarks are largely restricted to within-screen identity recovery. Here we introduce RevPert, a gallery-native reverse perturbation model that ranks a fixed genetic catalog for a query contrast {Delta}Y* = YB - YA by combining signed Pearson connectivity with a learned residual. Across Replogle Essential Perturb-seq (four lines) and LINCS-KO screens (ten lines), RevPert recovered held-out interventions at leading performance relative to matched baselines. Applied to public drug-resistance contrasts in HCC and CML, dual-arm ranking placed pre-specified disease anchors far higher on the expected arms than ranking the same signatures by differential-expression magnitude alone (Essential residual model for HCC; a transductive GWPS residual for CML). RevPert therefore couples within-screen reverse ranking to a screen-external signed-geometry check; the latter calibrates literature anchors and is not claimed as held-out recovery.",
  "abstract_shown": true,
  "affiliations": [
    "The No. 944 Hospital of Joint Logistic Support Force of PLA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Liang"
    },
    {
      "name": "C. Yang"
    },
    {
      "name": "J. Wang"
    },
    {
      "name": "y. Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dgmr0jwfxc57t.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.19.745674",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.19.745674v2.full",
  "id": "dgmr0jwfxc57t",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.19.745674v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7204126544",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.19.745674v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dgmr0jwfxc57t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:45Z",
      "native_id": "10.64898/2026.08.19.745674",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.19.745674v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "RevPert: predicting candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation",
  "title_text": "RevPert: predicting candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:45Z",
  "url": "https://research.draconitic.com/paper/dgmr0jwfxc57t",
  "venue": "bioRxiv",
  "versions": []
}